cortex_differential_expression_analysis
Mohit Rastogi
September 27, 2022
Last updated: 2022-09-27
Checks: 7 0
Knit directory: HumanMultiomics/
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Introduction
This is the differential expression analysis from the total RNA sequencing experiment performed on postmortem cortex obtained from Down syndrome and control individuals.
First, we will load all the packages required to perform this analysis
library(ggplot2)
library(dplyr)
library(edgeR)
library(openxlsx)
library(ggrepel)
library(DBI)
library(org.Hs.eg.db)
library("pcaExplorer")
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(rtracklayer)
library(ggbio)
library(clusterProfiler)
library(enrichplot)
library(ggridges)
library(karyoploteR)
library(tidyverse)
library(forcats)
library(pathview)
library(ComplexHeatmap)
library(circlize)
library(kableExtra)
library(DT)Read the raw count matrix for hippocampus samples
The count matrix consists of 12 control samples (C12-C23) and 10 Down syndrome samples (Ds10-Ds19)
C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 Ds10
ENSG00000186092 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000187634 5 4 0 21 61 28 21 15 27 38 23 29 20
ENSG00000188976 266 704 279 566 1561 2487 1438 1547 2030 1593 2350 2031 996
ENSG00000187961 20 35 4 30 149 138 129 164 142 158 181 174 92
Ds11 Ds12 Ds13 Ds14 Ds15 Ds16 Ds17 Ds18 Ds19
ENSG00000186092 0 0 0 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0 0 0 0
ENSG00000187634 63 45 16 36 10 24 50 41 32
ENSG00000188976 1214 1776 1759 1954 897 1112 694 771 1465
ENSG00000187961 361 130 76 148 117 107 123 66 166Define the groups
[1] Cont Cont Cont Cont Cont Cont Cont Cont Cont Cont Cont Cont DS DS DS
[16] DS DS DS DS DS DS DS
Levels: Cont DSDesign the model for performing differential expression analysis
Calculate counts per million in log
Visualize the log counts per million (cpm) as a heatmap
| Version | Author | Date |
|---|---|---|
| 4e1ab3a | mohit-rastogi | 2022-09-15 |
Perform PCA after loading the sample information
[1] TRUE-- removing the lower 10% of variables based on variance
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Filter the data based on heatmap, PCA and RIN values
Looking at the clustering and PCA plots, we remove few samples which look like outliers: C12,C15,C16,C18,Ds10,Ds14
C13 C14 C17 C19 C20 C21 C22 C23 Ds11 Ds12 Ds13 Ds15 Ds16
ENSG00000186092 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284733 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000284662 0 0 0 0 0 0 0 0 0 0 0 0 0
ENSG00000187634 4 0 28 15 27 38 23 29 63 45 16 10 24
ENSG00000188976 704 279 2487 1547 2030 1593 2350 2031 1214 1776 1759 897 1112
ENSG00000187961 35 4 138 164 142 158 181 174 361 130 76 117 107
Ds17 Ds18 Ds19
ENSG00000186092 0 0 0
ENSG00000284733 0 0 0
ENSG00000284662 0 0 0
ENSG00000187634 50 41 32
ENSG00000188976 694 771 1465
ENSG00000187961 123 66 166####Perform hierarchical clsutering and PCA on the filtered samples
[1] Cont Cont Cont Cont Cont Cont Cont Cont DS DS DS DS DS DS DS
[16] DS
Levels: Cont DS
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
[1] TRUE-- removing the lower 10% of variables based on variance
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Filter the genes based on their expression in n-1 samples
keep_cth_filt_coding2
FALSE TRUE
5787 14181 Perform normalization using TMM
group lib.size norm.factors
C13 Cont 22838780 0.9494258
C14 Cont 16449030 0.8350646
C17 Cont 34516167 1.0557014
C19 Cont 31663808 0.9980017
C20 Cont 33797811 1.0694944
C21 Cont 31634000 0.9990789
C22 Cont 38044140 1.0283538
C23 Cont 34010695 1.0189124
Ds11 DS 17964976 0.9749882
Ds12 DS 29708146 1.1038969
Ds13 DS 29318225 1.1023692
Ds15 DS 22564390 1.1062550
Ds16 DS 25861208 1.0729509
Ds17 DS 19634177 0.7869030
Ds18 DS 19396795 0.8939199
Ds19 DS 24608395 1.0793916Estimate differential expression using GLM
Iteration 1: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 2: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 3: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 4: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 5: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.
Iteration 6: Re-fitting GLM. Re-estimating trended dispersion.
Re-estimating tagwise dispersion.Number of differentially expressed genes in the hippocampus
group_cth_filt_coding2DS
Down 2779
NotSig 8633
Up 2769Counts per million (CPM) values for all the genes considered in the differential expression analysis
cpm_y_cth_filt_coding22<-cpm(y_cth_filt_coding2)
cpm_y_cth_filt.dt <- DT::datatable(cpm_y_cth_filt_coding22, rownames=TRUE, class="white-space: nowrap", escape=FALSE)
cpm_y_cth_filt.dtHere, we annotated the data from the previous using ENSEMBL information for each gene and mapped the DEGs onto their chromosomal location.
'select()' returned 1:many mapping between keys and columnsGRanges object with 5531 ranges and 12 metadata columns:
seqnames ranges strand | logFC logCPM LR
<Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric>
627 chr11 27654893-27722058 - | -2.10525 3.67477 65.3584
81543 chr21 44455510-44462196 + | 2.54708 1.46346 61.1630
1356 chr3 149162410-149221829 - | 3.40960 4.10031 61.1510
64114 chr2 218274197-218292586 - | 1.83696 5.90933 61.0578
25966 chr21 41885112-41954018 - | 1.20996 5.96190 56.4304
... ... ... ... . ... ... ...
9900 chr1 149903318-149917844 - | -0.592777 7.83472 5.45569
308 chr4 121667946-121696995 - | 0.403850 5.66521 5.45491
6480 chr3 186930325-187078553 + | 0.413191 5.69142 5.45285
10645 chr12 121237675-121298308 - | -0.450753 8.05114 5.45270
23592 chr12 65169583-65248355 + | -0.344752 5.31480 5.45104
PValue FDR ENSEMBL ENTREZID SYMBOL
<numeric> <numeric> <character> <character> <character>
627 6.24422e-16 8.85493e-12 ENSG00000176697 627 BDNF
81543 5.25394e-15 1.96490e-11 ENSG00000160233 81543 LRRC3
1356 5.28596e-15 1.96490e-11 ENSG00000047457 1356 CP
64114 5.54233e-15 1.96490e-11 ENSG00000135926 64114 TMBIM1
25966 5.82241e-14 1.48993e-10 ENSG00000157617 25966 C2CD2
... ... ... ... ... ...
9900 0.0195047 0.0498910 ENSG00000159164 9900 SV2A
308 0.0195134 0.0499044 ENSG00000164111 308 ANXA5
6480 0.0195364 0.0499494 ENSG00000073849 6480 ST6GAL1
10645 0.0195381 0.0499494 ENSG00000110931 10645 CAMKK2
23592 0.0195567 0.0499880 ENSG00000174106 23592 LEMD3
GENENAME CHR UNIPROT ALIAS
<character> <character> <character> <character>
627 brain derived neurot.. 11 A0A0E3SU01 ANON2
81543 leucine rich repeat .. 21 Q9BY71 C21orf102
1356 ceruloplasmin 3 A5PL27 CP-2
64114 transmembrane BAX in.. 2 A0A024R472 LFG3
25966 C2 calcium dependent.. 21 Q9Y426 C21orf25
... ... ... ... ...
9900 synaptic vesicle gly.. 1 B4E000 SV2
308 annexin A5 4 P08758 ANX5
6480 ST6 beta-galactoside.. 3 P15907 SIAT1
10645 calcium/calmodulin d.. 12 A0A024RBQ0 CAMKK
23592 LEM domain containin.. 12 Q9Y2U8 MAN1
-------
seqinfo: 595 sequences (1 circular) from hg38 genomeusing coord:genome to parse x scale
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 687a35a | mohit-rastogi | 2022-09-15 |
Results from differential expression analysis for cortex samples
results_rna_annotated_cth_filt_coding2.dt <- DT::datatable(results_rna_annotated_cth_filt_coding2, rownames=TRUE, class="white-space: nowrap", escape=FALSE)
results_rna_annotated_cth_filt_coding2.dt
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] DT_0.25.1
[2] kableExtra_1.3.4
[3] circlize_0.4.15
[4] ComplexHeatmap_2.8.0
[5] pathview_1.32.0
[6] forcats_0.5.2
[7] stringr_1.4.1
[8] purrr_0.3.4
[9] readr_2.1.2
[10] tidyr_1.2.1
[11] tibble_3.1.8
[12] tidyverse_1.3.2
[13] karyoploteR_1.18.0
[14] regioneR_1.24.0
[15] ggridges_0.5.3
[16] enrichplot_1.12.3
[17] clusterProfiler_4.0.5
[18] ggbio_1.40.0
[19] rtracklayer_1.52.1
[20] TxDb.Hsapiens.UCSC.hg38.knownGene_3.13.0
[21] GenomicFeatures_1.44.2
[22] GenomicRanges_1.44.0
[23] GenomeInfoDb_1.28.4
[24] pcaExplorer_2.18.0
[25] org.Hs.eg.db_3.13.0
[26] AnnotationDbi_1.54.1
[27] IRanges_2.28.0
[28] S4Vectors_0.32.3
[29] Biobase_2.54.0
[30] BiocGenerics_0.40.0
[31] DBI_1.1.3
[32] ggrepel_0.9.1
[33] openxlsx_4.2.5
[34] edgeR_3.34.1
[35] limma_3.50.1
[36] dplyr_1.0.10
[37] ggplot2_3.3.6
[38] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 svglite_2.1.0
[3] Hmisc_4.7-1 ps_1.7.1
[5] Rsamtools_2.8.0 foreach_1.5.2
[7] rprojroot_2.0.3 crayon_1.5.1
[9] MASS_7.3-58.1 nlme_3.1-159
[11] backports_1.4.1 reprex_2.0.2
[13] GOSemSim_2.18.1 rlang_1.0.5
[15] XVector_0.32.0 readxl_1.4.1
[17] irlba_2.3.5 SparseM_1.81
[19] callr_3.7.2 filelock_1.0.2
[21] GOstats_2.58.0 BiocParallel_1.28.3
[23] rjson_0.2.21 bit64_4.0.5
[25] glue_1.6.2 pheatmap_1.0.12
[27] rngtools_1.5.2 parallel_4.1.0
[29] processx_3.7.0 shinyAce_0.4.2
[31] shinydashboard_0.7.2 DOSE_3.18.3
[33] haven_2.5.1 tidyselect_1.1.2
[35] SummarizedExperiment_1.22.0 XML_3.99-0.10
[37] GenomicAlignments_1.28.0 xtable_1.8-4
[39] magrittr_2.0.3 evaluate_0.16
[41] cli_3.4.0 zlibbioc_1.40.0
[43] rstudioapi_0.14 whisker_0.4
[45] bslib_0.4.0 rpart_4.1.16
[47] fastmatch_1.1-3 ensembldb_2.16.4
[49] treeio_1.16.2 shiny_1.7.2
[51] BiocSingular_1.8.1 xfun_0.33
[53] clue_0.3-61 cluster_2.1.4
[55] tidygraph_1.2.2 TSP_1.2-1
[57] KEGGREST_1.32.0 biovizBase_1.40.0
[59] threejs_0.3.3 ape_5.6-2
[61] dendextend_1.16.0 Biostrings_2.60.2
[63] png_0.1-7 reshape_0.8.9
[65] withr_2.5.0 shinyBS_0.61.1
[67] bitops_1.0-7 ggforce_0.3.4
[69] RBGL_1.68.0 plyr_1.8.7
[71] cellranger_1.1.0 GSEABase_1.54.0
[73] AnnotationFilter_1.16.0 dqrng_0.3.0
[75] pillar_1.8.1 GlobalOptions_0.1.2
[77] cachem_1.0.6 fs_1.5.2
[79] GetoptLong_1.0.5 DelayedMatrixStats_1.14.3
[81] vctrs_0.4.1 ellipsis_0.3.2
[83] generics_0.1.3 NMF_0.24.0
[85] tools_4.1.0 foreign_0.8-82
[87] munsell_0.5.0 tweenr_2.0.2
[89] fgsea_1.18.0 DelayedArray_0.18.0
[91] fastmap_1.1.0 compiler_4.1.0
[93] httpuv_1.6.6 pkgmaker_0.32.2
[95] plotly_4.10.0 GenomeInfoDbData_1.2.6
[97] gridExtra_2.3 lattice_0.20-45
[99] deldir_1.0-6 AnnotationForge_1.34.1
[101] utf8_1.2.2 later_1.3.0
[103] BiocFileCache_2.0.0 jsonlite_1.8.0
[105] GGally_2.1.2 scales_1.2.1
[107] ScaledMatrix_1.0.0 graph_1.70.0
[109] sparseMatrixStats_1.4.2 tidytree_0.4.0
[111] genefilter_1.74.1 lazyeval_0.2.2
[113] promises_1.2.0.1 doParallel_1.0.17
[115] latticeExtra_0.6-30 checkmate_2.1.0
[117] rmarkdown_2.16 cowplot_1.1.1
[119] webshot_0.5.3 dichromat_2.0-0.1
[121] downloader_0.4 BSgenome_1.60.0
[123] igraph_1.3.4 survival_3.4-0
[125] yaml_2.3.5 systemfonts_1.0.4
[127] htmltools_0.5.3 memoise_2.0.1
[129] VariantAnnotation_1.38.0 BiocIO_1.2.0
[131] locfit_1.5-9.6 seriation_1.3.6
[133] PCAtools_2.4.0 graphlayouts_0.8.1
[135] viridisLite_0.4.1 digest_0.6.29
[137] assertthat_0.2.1 mime_0.12
[139] rappdirs_0.3.3 registry_0.5-1
[141] RSQLite_2.2.17 yulab.utils_0.0.5
[143] data.table_1.14.2 blob_1.2.3
[145] labeling_0.4.2 splines_4.1.0
[147] Formula_1.2-4 Cairo_1.6-0
[149] googledrive_2.0.0 OrganismDbi_1.34.0
[151] ProtGenerics_1.26.0 RCurl_1.98-1.8
[153] broom_1.0.1 hms_1.1.2
[155] modelr_0.1.9 colorspace_2.0-3
[157] base64enc_0.1-3 BiocManager_1.30.18
[159] shape_1.4.6 aplot_0.1.7
[161] nnet_7.3-17 sass_0.4.2
[163] Rcpp_1.0.9 fansi_1.0.3
[165] tzdb_0.3.0 R6_2.5.1
[167] lifecycle_1.0.2 zip_2.2.1
[169] curl_4.3.2 googlesheets4_1.0.1
[171] jquerylib_0.1.4 DO.db_2.9
[173] Matrix_1.4-1 qvalue_2.24.0
[175] RColorBrewer_1.1-3 iterators_1.0.14
[177] topGO_2.44.0 htmlwidgets_1.5.4
[179] bamsignals_1.24.0 beachmat_2.8.1
[181] polyclip_1.10-0 biomaRt_2.48.3
[183] crosstalk_1.2.0 shadowtext_0.1.2
[185] gridGraphics_0.5-1 rvest_1.0.3
[187] htmlTable_2.4.1 patchwork_1.1.2
[189] KEGGgraph_1.52.0 codetools_0.2-18
[191] matrixStats_0.62.0 lubridate_1.8.0
[193] GO.db_3.13.0 getPass_0.2-2
[195] prettyunits_1.1.1 dbplyr_2.2.1
[197] gridBase_0.4-7 gtable_0.3.1
[199] git2r_0.30.1 highr_0.9
[201] ggfun_0.0.7 httr_1.4.4
[203] stringi_1.7.8 progress_1.2.2
[205] reshape2_1.4.4 farver_2.1.1
[207] heatmaply_1.3.0 annotate_1.70.0
[209] viridis_0.6.2 Rgraphviz_2.36.0
[211] ggtree_3.0.4 xml2_1.3.3
[213] bezier_1.1.2 restfulr_0.0.15
[215] interp_1.1-3 geneplotter_1.70.0
[217] ggplotify_0.1.0 Category_2.58.0
[219] DESeq2_1.32.0 bit_4.0.4
[221] scatterpie_0.1.8 jpeg_0.1-9
[223] MatrixGenerics_1.4.3 ggraph_2.0.6
[225] pkgconfig_2.0.3 gargle_1.2.1
[227] knitr_1.40